Research Outputs

Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2

Browse

Filters

2008 - 200912010 - 20111

Advanced Search

Filter by

Settings

Department: search.filters.department.Department of MathematicsSubject: search.filters.subject.Biomedical engineering

Search Results

Now showing 1 - 2 of 2
  • Placeholder
    PublicationMetadata only
    Quantization and analysis of hippocampal morphometric changes due to dementia of alzheimer type using metric distances based on large deformation diffeomorphic metric mapping
    (Elsevier, 2011) Beg, Mirza Faisal; Ceritoglu, Can; Wang, Lei; Morris, John C.; Csernansky, John G.; Miller, Michael I.; Ratnanather, J. Tilak; Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/A
    The metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm is used to quantize changes in morphometry of brain structures due to neuropsychiatric diseases. For illustrative purposes we consider changes in hippocampal morphometry (shape and size) due to very mild dementia of the Alzheimer type (DAT). LDDMM, which was previously used to calculate dense one-to-one correspondence vector fields between hippocampal shapes, measures the morphometric differences with respect to a template hippocampus by assigning metric distances on the space of anatomical images thereby allowing for direct comparison of morphometric differences. We characterize what information the metric distances provide in terms of size and shape given the hippocampal, brain and intracranial volumes. We demonstrate that metric distance is a measure of morphometry (i.e., shape and size) but mostly a measure of shape, while volume is mostly a measure of size. Moreover, we show how metric distances can be used in cross-sectional, longitudinal analysis, as well as left-right asymmetry comparisons, and provide how the metric distances can serve as a discriminative tool using logistic regression. Thus, we show that metric distances with respect to a template computed via LDDMM can be a powerful tool in detecting differences in shape. (C) 2011 Elsevier Ltd. All rights reserved.
  • Placeholder
    PublicationMetadata only
    Segmentation of arteries in mprage images of the ventral medial prefrontal cortex
    (Elsevier, 2008) Penumetcha, N.; Jedynak, B.; Hosakere, M.; Botteron, K. N.; Ratnanather, J. T.; Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/A
    A method for removing arteries that appear bright with intensities similar to white matter in Magnetized Prepared Rapid Gradient Echo images of the ventral medial prefrontal cortex is described. The Fast Marching method is used to generate a curve within the artery. Then, the largest connected component is selected to segment the artery which is used to mask the image. The surface reconstructed from the masked image yielded cortical thickness maps similar to those generated by manually pruning the arteries from surfaces reconstructed from the original image. The method may be useful in masking vasculature in other cortical regions. (c) 2007 Elsevier Ltd. All rights reserved.